Next, we used an MR Transformer [1]} to implement a sentence selector model. Our MR approach achieves 0.881 of F1 score on MR task (showing competitive results on the SQuAD leaderboard with respect to single models).
Then, we simply select the sentence from which the MR extracts the answer span to solve the AS2 task on SQuAD: the model achieves a P@1 of 0.952.
 REF  shows that such model (straight line) is comparable to our baseline (single Transformer models), whereas Loc_t achieves better performance.
This is a loose comparison but it suggests that our approach may be applied to develop new MR methods.
<FIGURE>